Refactoring of static machine-readable codes

ABSTRACT

Methods, devices and systems for computing interactive customized content in response to a scan, or multiple scans, of a machine-readable label are provided. Illustrative methods may include receiving, from a user or group of users, a scan of a machine-readable optical label captured using a camera of a mobile device. Methods may include determining, based on the scan or scans, one, or more than one, redirect Uniform Resource Locator(s) (URL(s)). Methods may include generating, based on a user profile or group of user profiles and a redirect service that is accessed using the redirect URL, customized content associated with the scan. Methods may include redirecting the user or users to one or more target landing page URL(s), and providing to the user or users, through a browser on the mobile device or mobile devices, one or more than one target landing page(s) that includes the customized content.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of nonprovisional U.S. patentapplication Ser. No. 17/399,285 filed on Aug. 11, 2021 which is acontinuation-in-part of nonprovisional U.S. patent application Ser. No.17/178,955 filed on Feb. 18, 2021, which issued as U.S. Pat. No.11,120,095, which is hereby incorporated by reference herein in itsentirety, said U.S. patent application Ser. No. 17/178,955 which is anonprovisional of U.S. Provisional Patent Application No. 62/978,136filed on Feb. 18, 2020, said U.S. Provisional Patent Application No.62/978,136 which is hereby incorporated by reference herein in itsentirety.

FIELD OF TECHNOLOGY

This disclosure relates to digital platforms, and more particularly togenerating customized digital content in response to scanning amachine-readable label.

BACKGROUND

Machine-readable labels, such as quick-response (“QR”) codes, providebusinesses with a fast and efficient medium for connecting withconsumers. Instructions, such as URLs, contact information and otheralphanumeric information may be encoded in QR codes. Businesses mayleverage QR codes to guide consumers to a desired destination (real orvirtual) where the customers can access products, services andinformation provided by the business. However, a scanning device isneeded to scan and interpret the instructions encoded in a QR code.

In 2010, 62.6 million people in the United States used smartphones. In2020 that number is predicted to more than quadruple to 272.6 million.Commensurate with increased smartphone use is integration of technologyinto smartphones that scans and interprets machine-readable labels.Today, many smartphones include a native camera application thatrecognizes machine-readable labels such as QR codes. There is no need todownload and install a separate application or use a separate reader toscan a machine-readable label.

Machine-readable labels now potentially offer an inexpensive means ofproviding consumers with easy access to products, services orinformation. Consumers are already using their smartphones to search forinformation about a product/service of interest. Now, businesses can tapinto this tendency by using machine-readable labels to guide consumersto targeted content associated with a product/service. Furthermore,machine-readable labels are inexpensive and easy to print on a varietyof surfaces such as business cards, product packaging, posters ormarketing materials.

Each user, or group/cohort of users, retrieves the same information froma single label. This limits the flexibility of the information derivedfrom each label as will be explained in more detail below. Throughoutthis application, the term “user” should be understood to refer toeither a singular user or, in the alternative, a plurality of users. Theplurality of users is also referred to herein, in the alternative, as agroup of users or cohort of users.

The user that scans a label, conventionally, will be directed to anidentical URL or process the identical information encoded within the QRcode. However, each user may have different needs or interests. A firstuser may be interested in contacting customer service for a questionabout a product. A second user may be interested in purchasing a newproduct. A third user may be interested in returning a product. Yet eachuser will retrieve the same instructions from a single label related tothe product.

Another hurdle facing widespread adoption of machine-readable labels isthat the instructions encoded in such labels are not easily changed.Contact information or a URL associated with a business may change. Yet,the machine-readable label may still encode old contact information oran old URL. Machine-readable labels may be printed and affixed to realproperty. It is costly to remove these machine-readable labels andreplace them with updated labels.

It would be desirable to provide apparatus and methods for refactoringmachine-readable labels such as QR codes to provide customized contentto each user that scans or extracts information from a machine-readablelabel. Accordingly, it is desirable to provide apparatus and methods forREFACTORING OF STATIC MACHINE-READABLE CODES.

SUMMARY

Embodiments of the disclosed technology relate to providing customizeddigital content for interactions between two or more parties. Suchinteractions may include, but not be limited to, commercial salestransactions between a buyer and a seller, engagement actions between areader and a publisher, advertising interactions between a consumer andan advertiser. The disclosed embodiments can, for example, be used indirect-to-consumer (“DTC”) markets and DTC retail transactions.

In an exemplary aspect, a method for providing a user with customizeddigital content on a mobile device is disclosed. Methods includesreceiving, from the user, information encoded in a machine-readableoptical label. The information may be captured optically, using a cameraof the mobile device. The information may be captured in any suitablemanner. For example, the information may be captured using near fieldcommunication (“NFC”), Bluetooth, 5G communication between themachine-readable label and the user's mobile device. Methods may includeusing any suitable technology or protocol for capturing informationencoded in a machine-readable label. Any suitable technology forcapturing information encoded in a machine-readable may be referred toherein as a “scan” of a machine-readable label. A device that capturesinformation encoded in a machine-readable may be referred to herein as a“scanning device.”

Methods may include, determining, based on scanning information encodedin a label, a redirect Uniform Resource Locator (“URL”). Methods mayinclude determining, based on a user profile or a cohort profile and aredirect service, a landing page URL.

A redirect service may be accessed using the redirect URL. For example,the machine-readable label may encode instructions that trigger thescanning device that captures the encoded information to perform atarget action or function. Methods may include providing a landing pageto the user through a browser on the scanning device. The landing pagepresented in the browser may be based on the landing page URL. Thelanding page may include content customized based on one or more scanevent details associated with the scan of the machine-readable opticallabel.

In yet another exemplary aspect, the above-described methods areembodied in the form of processor-executable code and stored in acomputer-readable program medium. In yet another exemplary embodiment, adevice that is configured or operable to perform the above-describedmethods is disclosed. The above and other aspects and theirimplementations are described in greater detail in the drawings,disclosure, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and advantages of this disclosure will be apparent uponconsideration of the following disclosure, taken in conjunction with theaccompanying drawings, in which like reference characters refer to likeparts throughout, and in which:

FIG. 1 shows an illustrative machine-readable optical label inaccordance with principles of the disclosure;

FIG. 2 shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 3A shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 3B shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 4A shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 4B shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 5 shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 6 shows an illustrative system in accordance with principles of thedisclosure;

FIG. 7 shows an illustrative system in accordance with principles of thedisclosure;

FIG. 8 shows an illustrative process in accordance with principles ofthe disclosure;

FIG. 9 shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 10 shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 11 shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 12 shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 13 shows an illustrative machine-readable label in accordance withprinciples of the disclosure;

FIG. 14 shows an illustrative machine-readable label in accordance withprinciples of the disclosure;

FIG. 15 shows an illustrative machine-readable label in accordance withprinciples of the disclosure;

FIG. 16 shows an illustrative system and process in accordance withprinciples of the disclosure;

FIG. 17 shows an illustrative system and process in accordance withprinciples of the disclosure; and

FIG. 18 shows illustrative information generated in accordance withprinciples of the disclosure.

DETAILED DESCRIPTION

Embodiments of the disclosed technology may be leveraged to deliver atruly exceptional user experience in response to scanning amachine-readable label. The disclosed technology may include generatedcustomized content for specific user needs, interests, and situations.The disclosed technology may include generated customized content thatis continually updated based on a user engagement.

For example, in response to a scan of a machine-readable label, machinelearning algorithms may utilize a user profile or user persona todynamically generate customized content for the user in real-time. Anexemplary user profile may include user preferences or cohortpreferences, such as previous purchasing and browsing activity of theuser and the user demographic information.

An artificial intelligence (“AI”) method for dynamically generatingcustomize content for a user is provided. The content may include atarget landing page. The target landing page may be a webpage that isgenerated in response to scanning of a machine-readable optical label.

An illustrative machine-readable label may include a plurality ofmodules. A module may be a dark module or a light module. A scanningdevice, such as a mobile device, may be configured to interpretinstructions encoded by a pattern of light and dark modules. A camera ofthe mobile device may be used to capture a scan of the machine-readablelabel. The scanning device may interpret the pattern of modules. Forexample, a light module may represent a 0, and a dark module mayrepresent a 1.

A machine-readable label may be a label such as a Quick Response (“QR”)code or a linear barcode that is read optically. A machine-readablelabel may transmit information encoded in the label using any suitablesignal or communication protocol that is received by a scanning device.For example, a machine-readable label may include an embedded passive oractive radio-frequency identification (“RFID”) tag. A machine-readablelabel may transmit encoded information over a cellular network. Amachine-readable label may transmit encoded information over a Wi-Finetwork.

The machine-readable label may be “static.” A “static” label may encodea single set of instructions that are presented to multiple users and/ormobile devices. A static label may encode any suitable information suchas a Uniform Resource Locator (“URL”), contact information associatedwith a business or other alphanumeric information and instructions thattrigger the scanning device to perform a target action or function(possibly including sending an e-mail or a text).

For example, information encoded in a scanned label may triggerlaunching of a web browser resident on the scanning device and loadingof a target landing page. The information encoded in a scanned label maytrigger any suitable function. Other illustrative functions may includeinitiating a phone call or a video conference, launching an email/textapplication on the scanning device and formulating a pre-formattedmessage to a target destination.

A machine-readable label may be presented on any suitable substrate. Forexample, a static machine-readable label may be printed on a sticker,displayed on a billboard, generated on a mobile device, displayed duringa TV broadcast, or embedded in a video. A static machine-readable labelmay present the single set of instructions regardless of the substrateused to present the static label.

AI methods may include receiving a scan of a static machine-readableoptical label. The scan may be captured using a camera of a mobiledevice. The mobile device may be operated by a user. Methods may includedetermining, based on the captured scan, a redirect URL. Methods mayinclude interpreting the instructions encoded by a pattern of light anddark modules in the static machine-readable optical label. The encodedinstructions may include a redirect URL.

After the scanning device extracts the redirect URL, methods may includedetermining a landing page. The landing page may include contentcustomized for the user of the scanning device. The landing page mayinclude content customized for the display on the mobile device used toscan the static machine-readable optical label. The landing page mayinclude content customized for the user based on scan event details.Illustrative scan event details may include a scan time, scan location,weather at the scan time, user physiological characteristics,(fingerprint, facial scan, heart rate) and user demographic information.Scan event details may be determined by the mobile device that scans thestatic machine-readable optical label. For example, the scanning devicemay capture timestamp, GPS location and/or a user facial scan. Otherevent details may include day of week included in the time stampassociated with the scan, day of month included in the time stampassociated with the scan, time of day included in the time stampassociated with the scan, proximity to a certain holiday or otherpre-determined day, scanning device, type of scanning device, operatingsystem of scanning device, relativity to current events which may bedetermined from news or other information sources, or other suitableevent details. Type of scanning device may include such details asmanufacturer of the scanning device, version number of the scanning orother relevant details associated with the scanning device.

The customized content may be determined based on a profile of the user.A user profile may include multiple attributes representative of theuser. The multiple attributes may be stored within a multi-dimensionalmatrix, a graph or any suitable data structure. Data structures maystore attributes associated with multiple users.

Attributes representative of a user may include user preferences, scanevent details, previous purchasing and browsing activity of the user,scanning devices used by the user, a type of scanning device used by theuser, user demographic information and user behavioral information. Forexample, the user profile may be determined based on a browsing andpurchasing history of the user (e.g., which websites or online chatroomshas the user frequented in the last 30, 60 or 90 days), user preferences(e.g., favorite brands, favorite movies or TV shows, etc.), and userdemographic information (e.g., gender, age, marital status, income,etc.). Data structures may store relationships linking attributes ofmultiple users within a cohort. For example, attributes representativeof a user within a cohort may include attributes or profiles associatedwith other users within a cohort that share one or more attributesincluded in the profile of the scanning user. Attributes may includescan event details.

Methods may include computing correlations between attributes stored inthe multi-dimensional matrix and/or a user profile. Methods may includecomputing correlations among attributes stored in the multi-dimensionalmatrix and/or a user profile and a given set of scan event details.Based on the computed correlations, methods may include determining alanding page for a user. A feedback mechanism may dynamically update themulti-dimensional matrix and/or user profile based on the currentengagement (or the most recent N engagements) of the user with a landingpage or other activity performed using the mobile device.

For example, a machine-readable label may encode a link to a targetwebpage. The target webpage itself may include a plurality of links toother webpages. The links may connect a first user to one or more socialmedia pages of a second user. The links may connect the first user toother webpages, video, audio, or any suitable content that may be linkedto a webpage.

When the first user scans the label, methods may include determiningwhich of the links included on the target webpage would be of greatestinterest to the first user. Methods may include ordering the linksassociated with the target webpage based on scan event detailsassociated with the scanning of the label. Methods may include computingthe order of the links based on one or more relationships linking scanevent details and attributes stored in the multi-dimensional matrix.

Methods may include computing the ordering of the links using machinelearning algorithms to compute a level interest to the first user foreach of the links. A level of interest may be computed based on aprofile of the first user. Methods may include ordering the linksdisplayed to the first user on the target webpage. After determining theordering of links on the target webpage, a landing page may be displayedon a browser of the first user's scanning device. The landing pagedisplayed to the first user may display the links on the target webpagein the order determined based on a user profile associated with thefirst user.

For example, a landing page presented to the user in response to thescan may display links of the target webpage that are determined to beof greatest interest to the first user. The links of the target webpagemay be displayed on top of the landing page.

Links that are determined to be of lesser interest to the first user maybe displayed lower on the landing page displayed to the first user. Whenpresented with a landing page that includes the ordered links, methodsmay include monitoring activity of the first user on the landing page.If the first user accesses a link presented on the landing page that isnot the first or uppermost link, methods may include dynamicallyreordering the links displayed on the landing page.

Methods may include reordering the links on the landing page in responseto receiving a second scan of the label or another label that encodes alink to the target webpage. Links displayed to the first user on alanding page may be reordered based on which link or links the firstuser accessed in response to an earlier scan that triggered a display oflinks on the target webpage. The links presented in a landing page maybe reordered based on updates (e.g., adding new attributes or updates topreviously stored attributes) to the user's profile. The user's profilemay be updated in response to the user's selection of a link or linksdisplayed on the landing page. The user's profile may be updated inresponse to the user's selection of a link or links displayed on thelanding page accessed directly by the first user by typing a URL for thetarget webpage directly into a browser.

The links associated with the target webpage may be reordered on alanding page based on updates to the multi-dimensional matrix storingattributes for two or more users. The multi-dimensional matrix may beupdated in response to actions of other users. The actions of the otherusers may include accessing links on the target webpage.

When a second user scans the label, or another label that encodes a linkto the target webpage, methods may include computing an order of linksfor the second user. The order of links for the second user may bedetermined based on a profile associated with the second user. The orderof links may be displayed to the second user on a landing page presentedto the second user within a browser of the second user's scanningdevice. The order of links presented to the second user may be differentfrom the order of links displayed to the first user.

A user profile may graphically connect the user's offline activity tothe user's online activity. A graphical connection linking offlineactivity to online activity may be termed a Consumer Intent Graph(“CIG”). A CIG may be computed for multiple users. Such a CIG may linkoffline activity associated with a static label to online activityassociated with multiple users. For example, the CIG may includeconnections linking geographical positions of scanned labels and actiontaken by user in response to landing pages generated based on scans ofthe label at the geographic locations.

The CIG may link trackable electronic activity of users in response toscans of a static machine-readable label. The CIG may link trackableelectronic activity of users that scan a static machine-readable label.A static label may be positioned at a fixed location or on a specificobject (e.g., a bicycle, hat, shirt, car). The CIG may be designed toindex and rank content or actions to present to a user in response to ascan of a label. A CIG may include attributes that represent potentialactions or content that may be presented to a user.

An intent index score may be computed based on a CIG. The consumerintent index scores may include probabilistic scores that predict auser's likely intent to take a specific action or utilize specificcontent in response to scanning a label. For example, the intent indexscore for a specific action may have a range of 0 (unlikely to happen)to 100 (likely to happen).

A unique CIG may be formulated for each user. For example, a proprietormay operate a pizza restaurant in a mall. The proprietor may link twopotential offers to a static machine-readable optical label. The firstoffer may be a “two for one” dine-in deal. The second offer may be a“free home delivery.” The proprietor may position copies of the staticlabel at different locations throughout the mall.

A first user may be in the mall at 11:51 am (e.g., lunchtime). The firstuser may scan a static label displayed at a location in the mall withina threshold distance of the pizza restaurant operated by the proprietor.The scanning of the label may submit a URL encoded in the scanned labelto a redirect service. The redirect service may be an applicationresident on the mobile device of the first user used to scan the label.The redirect service may be a cloud-based application hosted on a remotecomputer server.

In addition to a redirect URL encoded in the scanned label, the scanningdevice may also transmit scan event details to the redirect service.Illustrative scan event details may include a scan time (e.g., 11:51 am)a scan day of week, a scan day of month, a scan location (e.g., specificlocation in the mall) scanning device (e.g., media access control(“MAC”) address of the scanning device or a type of scanning device). Inresponse to receiving the redirect URL encoded in the scanned label, theredirect service may formulate content, or an action associated with theredirect URL for the given set of scan event details.

In the above example, the redirect service may determine the first userhas a consumer intent index score of 93/100 (relatively high) withrespect to taking action regarding the available first offer for a“dine-in pizza” offer associated with the scanned label. The redirectservice may determine the first user has a consumer intent index scoreof 21/100 with respect to taking action regarding the available secondoffer for “deliver pizza.”

In an example related to cohort behavior, a first cohort may be in themall on Sunday. The first cohort—i.e., all members of a first group ofscanning devices, where said group is defined by one or more eventdetails or other metrics—may scan a static label displayed at a locationin the mall within a threshold distance of the pizza restaurant operatedby the proprietor. The scanning of the label may submit a URL encoded inthe scanned label to a redirect service. The redirect service may be anapplication resident on the mobile devices of each member of the firstcohort that scanned the label. The redirect service may be a cloud-basedapplication hosted on a remote computer server.

An exemplary redirect service may utilize a user profile associated withthe first user to determine which offer associated with a scanned labelto present to the first user. The redirect service may utilize scanevent details and the user profile to determine which offer associatedwith a scanned label to present to the first user. Scan event detailsmay be captured by a cookie resident on the first user's browser. Scanevent details may be captured by a pixel resident on a webpage visitedby the first user. Scan event details captured by a cookie or pixel maybe stored on the first user's mobile device. Scan event details capturedby a cookie or pixel may be stored within the user's profile orgenerally in, a multi-dimensional matrix.

Scan event details may include a unique identifier associated with thefirst user. The unique identifier may link the first user's mobiledevice to the first user's profile. Scan event details may include scantime, scan day of week, scan day of month, scan location, scan eventweather (gathered from external sources or the consumer's mobiledevice), scanning device, type of scanning device, or any other suitableevent details.

The redirect service may apply a refactoring algorithm to the userprofile and scan event details to formulate an action or content topresent to the first user. The refactoring algorithm may employ machinelearning or other forms of artificial intelligence to determineactions/content to present to the first user. Illustrative machinelearning techniques that may be employed by the refactoring algorithm ofthe redirect service include AdaBoost, Naive Bayes, Support VectorMachine, Random Forests, Artificial Neural Networks, Deep NeuralNetworks and Convolutional Neural Networks.

Based on information encoded in the scanned label, associated scan eventdetails, user profile and other suitable information stored in themulti-dimensional matrix, the refactoring algorithm may compute anintent index score for each potential action/content associated with thescanned label. Based on the intent index score for each potentialaction/content, the refactoring algorithm may present the action/contenthaving the highest intent index score to the consumer. The refactoringalgorithm may present the highest scoring action/content by redirectingthe user's browser to a URL of a webpage that presents the highestscoring action/content.

In the aforementioned example, the first user may be presented with thefirst “two for one” offer because this first offer scored higher(93/100) relative to the second “dine-in pizza” offer associated withthe scanned label. The intent index score computed by the refactoringalgorithm may be accurate than other digital advertising or internetsearch index scores.

The highest intent index score computed by the refactoring algorithmindicates the first user is contextually ready to take an action. In theabove example, the refactoring algorithm may assign the first “two forone” offer the higher intent index score because the first user is atthe mall (location) at lunch time (time), has purchased pizza before(user profile), and has scanned an optical label associated with a pizzarestaurant (data encoded in label).

Methods may include providing a redirect service. The redirect servicemay be accessed using a redirect URL. The redirect URL may be a URLencoded in a scanned label. When scanned, a native label scanningsoftware running on the mobile device may direct a browser on the user'smobile device to a webpage corresponding to the redirect URL. Thewebpage associated with the redirect URL may be associated with theredirect service. When the redirect service receives a request from thebrowser to provide content associated with the redirect URL, beforeproviding the content requested by the browser, the redirect service mayformulate a target landing page for the user that scanned the label.

The redirect service may utilize a refactoring algorithm to formulatethe target landing page based on the user's profile, scan event details,intent index score and a multi-dimensional matrix. The redirect servicemay generate a URL for the target landing page. The redirect service maydirect a browser on the user's mobile device to the target landing pageby redirecting the browser to the target landing page URL generated forthe target landing page. The redirect service may formulatecontent/action included in the target landing page using a deep neuralnetwork.

The redirect service may track one or more actions of the user on thetarget landing page. For example, using machine learning algorithms suchas a deep neural network and, based on the one or more scan eventdetails, the redirect service may dynamically update the user's profile.Input to the deep neural network may include information culled from themulti-dimensional matrix. The deep neural network may include a longshort-term memory (“LSTM”) architecture.

Information culled from the multi-dimensional matrix and utilized by theredirect service may be identified based on applying a machine learningalgorithm to compute correlations between information stored in themulti-dimensional matrix and a user profile. The information culled fromthe multi-dimensional matrix may be identified based on applying amachine learning algorithm to compute correlations between informationstored in the multi-dimensional matrix and the received scan eventdetails.

A target landing page computed by the redirect service may be one or aplurality of target landing pages. A user profile may be updated basedon actions of the user on each of the plurality of target landing pages.In some embodiments, the redirect service may dynamically change thetarget landing page in response to action of the user on the targetlanding page. Changing the target landing page may include reorderinglinks or altering content presented on the target landing page. Changingthe target landing page may include redirecting the user to a differenttarget landing page.

A target landing page associated with a label may be changed based onperformance metrics of the landing page. Performance metrics may includetime users spend on a presented landing page and user engagement withcontent presented on a landing (interaction with a chatbot or add tocart or other activity available on landing page).

Methods may include extracting a geo-physical location embedded in amachine-readable label. The geo-physical location may be a scan eventdetail. Methods may include determining a target landing page for a userthat scans the label based on the extracted location.

An artificial intelligence (“AI”) method is provided for providing auser with an interactive customized digital platform on a mobile device.Methods may include receiving, from the user, a first scan of a staticmachine-readable optical label. The first scan may be captured using acamera of the mobile device. The first scan may be associated with firstscan event details. Illustrative first scan event details may include afirst time and a first location. The first time may be when the firstscan of the label was captured by the user's mobile device. The firstlocation may be a location where the label was scanned by the user.

Methods may include determining, based on (1) a user profile, (2) thefirst time (3) the first day of the week and (4) the first location, afirst landing page URL. Methods may include redirecting a browser on themobile device to the first landing page URL. The first landing page URLmay provide access to content on a first target landing page.

Methods may include receiving, from the user, a second scan of thelabel. The second scan may be associated with second scan event details.Exemplary second scan event details may include a second time, a secondday of the week and a second location. The second time may be when thesecond scan of the label was captured by the user's mobile device. Thesecond location may be a second location where the staticmachine-readable optical label was scanned by the user.

Methods may include determining, in real-time and based on the userprofile, the second time, the second day of the week and the secondlocation, a second landing page URL. Methods may include redirecting thebrowser on the mobile device to a second target landing page, based onthe second landing page URL. The content associated with the secondtarget landing page may be different from content associated with thefirst target landing page. Methods may include utilizing a redirectservice to determine the first and the second target landing pages. Theredirect service may be run locally on the scanning mobile device. Theredirect service may be run at a location remote from the scanningmobile device.

After the first scan, methods may include incorporating the first time,the first day of the week, the first location and the first targetlanding page into the user profile. After the second scan, methods mayinclude incorporating the second time, the second location and thesecond target landing page into the user profile.

The scanned label may be a first static machine-readable optical label.Methods may include receiving, from the user, a scan of a second staticmachine-readable optical label. The second label may encode the sameinformation as the first label. The second label may encode informationthat is different from information encoded in the first label. Based onthe user profile, methods may include determining, in real-time and inresponse to the scan of the second label, a third landing page URL.Based on the third landing page URL, methods may include redirecting thebrowser on the user's mobile device to a third target landing page. Thethird landing page may include content computing based on the scan eventdetails associated with the scan of the second label.

Real time may be defined as ≤100 milliseconds from a time the label isscanned using the camera of the user's mobile device. Delayingredirecting of the user to a target landing page for longer than 100milliseconds after scanning may cause a delay noticeable by a humanuser. To minimize latency, the redirect service may utilize cloudcomputing services to process received scans at an edge node closestgeographically to the scanning mobile device.

Methods may include receiving, from the user, a third scan of the label.For example, the user may scan the first label two or more times.Methods may include redirecting the browser on the user's mobile deviceto the third target landing page in response to the third scan of thefirst label. For example, based on the scan event details associatedwith the third scan, the redirect service may determine that contentassociated with the third target landing page would maximize utility tothe user over any content that may be associated with the URL encoded inthe scanned label.

An artificial intelligence (“AI”) method for dynamically redirecting aplurality of scans of a machine-readable optical label to differentlanding pages is provided. The machine-readable label may be a staticlabel. Methods may include receiving a first scan of a static label. Thefirst scan may be received from a first mobile device or plurality ofmobile devices associated with a cohort of users.

In response to receiving the first scan, methods may include generatingcontent for a first target landing page. The content for the firsttarget landing page may be generated by a redirect service. The redirectservice may generate the content in real-time from a time a scanningdevice captured the first scan. Generating the content may includereordering links on a target landing page. Generating the content mayinclude computing an intent index score for content associated with thescanned static label.

Real-time may be defined as ≤100 milliseconds from a time the first scanwas captured by a scanning device. A computational delay by the redirectservice of longer than 100 milliseconds may be noticeable by a user ofthe scanning device. The user may notice a delay of ≥100 millisecondswhile displaying content after scanning the static label.

Methods may include generating a first target landing page URL. Thefirst target landing page URL may provide a link to the target landingpage generated by the redirect service. Methods may include redirectinga browser of the first mobile device to a first target landing pagecorresponding to the first target landing page URL.

Methods may include receiving, from a second mobile device (or secondcohort of mobile devices), second scan of the static label. The secondscan may be received by the redirect service. The static label scannedby the second mobile device may be the same static label scanned by thefirst mobile device. The static label scanned by the second mobiledevice may encode the same information as the static label scanned bythe first mobile device.

In response to receiving the second scan, methods may include generatingcontent for a second target landing page. The content for the secondtarget landing page may be generated by the redirect service. Theredirect service may generate the content in real-time from a time thesecond mobile device captured the second scan of the static label.

Generating the content in response to a scan may include reorderinglinks on a target landing page. Generating the content in response tothe second scan may include computing an intent index score for contentassociated with the static label scanned by the second mobile device. Inresponse to the second scan, methods may include generating a secondtarget landing page URL. Methods may include redirecting a browser ofthe second mobile device to a second target landing page correspondingto the second target landing page URL. The second target landing pagemay present content generated by the redirect service in response toreceiving the second scan.

The first target landing page generated in response to the first scanmay include content that is different from content generated in responseto the second scan. For example, the first target landing page maycorrespond to a social media profile and the second target landing pagemay correspond to a virtual reality construct. In some embodiments, thefirst target landing page may be identical to the second target landingpage. For example, the redirect service may determine that a first userprofile associated with the first mobile device shares at least oneattribute with a second user profile associated with the second mobiledevice.

The first scan may be associated with a first set of scan event details.The first set of first scan event details may identify the first mobiledevice (or type of mobile device), identity of a first user of the firstmobile device, a time the first scan was captured by the first mobiledevice, a day of the week the first scan was captured by the firstmobile device, a location of the first mobile device at the time of thefirst scan and/or a location of the label scanned by the first mobiledevice. The redirect service may compute one or more relationshipsbetween the first set of scan event details and information stored inthe multi-dimensional matrix to generate content in response to thefirst scan.

The second scan may be associated with a second set of scan eventdetails. The second set of scan event details may include the secondmobile device (or type of mobile device), identity of a second user ofthe second mobile device, a time the second scan was captured by thesecond mobile device, a day of the week the second scan was captured bythe second mobile device, a location of the second mobile device at thetime of the second scan and/or a location of the label scanned by thesecond mobile device. The redirect service may compute one or morerelationships between the second set of scan event details andinformation stored in the multi-dimensional matrix to generate contentin response to the second scan.

Methods may include receiving a third scan of the static label capturedby the first mobile device at a second time or day of the week or duringa pre-determined window of time. The third scan may be received by theredirect service. Methods may include generating, in real-time, thesecond target landing page URL in response to receiving the third scan.

Methods may include transmitting the second target landing page URL tothe first mobile device in response to receiving the third scan andthereby redirecting the browser of the first mobile device to the secondtarget landing page. When the first mobile device captures the thirdscan at the second time, the redirect service may determine that thesecond target landing page (also provided to the second mobile device)includes content most relevant to the user of the first mobile device.

In response to receiving the first scan from the first mobile device,methods may include applying the redirect service to dynamically updatea user profile associated with the first mobile device. The redirectservice may update the user profile based on input received from thebrowser on the first mobile device after the browser is redirected tothe first target landing page.

For example, methods may include generating a tracking pixel. Thetracking pixel may be embedded in the first target landing page. Thetracking pixel may be embedded in the first target landing page beforethe first user is redirected to the first target landing page. Thetracking pixel may capture activity of the first user on the firsttarget landing page. Methods may include dynamically updating the userprofile based on information captured by the tracking pixel andtransmitted to the redirect service.

Methods may include updating a user profile based on a location of thefirst mobile device at the time the first scan is captured by the firstmobile device. Methods may include updating the user profile based on alocation of the first mobile device after redirecting the browser on thefirst mobile device to the first target landing page. Methods mayinclude updating the user profile based on a browsing history stored inthe browser on the first mobile device.

An artificial intelligence (“AI”) redirect system for dynamicallygenerating a customized landing page is provided. The redirect systemmay dynamically generate a customized landing page in response toreceiving a scan of a machine-readable label. The machine-readable labelmay be a static label. The redirect system may include a processor and anon-transitory memory with instructions stored thereon. Theinstructions, when executed by the processor, may cause the processor toredirect a mobile device to target content formulated in response toreceiving the scan of the static label. The redirect system may beresident as an application on a mobile device.

The redirect system may capture a scan of the static label. The redirectsystem may extract a default Uniform Resource Locator (“URL”), or otherinformation encoded in the scanned static label. The redirect system maytransfer the default URL to a redirect service. The redirect system maytransfer one or more scan event details to the redirect service.

The redirect system may receive a redirect URL formulated by theredirect service. The redirect system may load a target landing pagecorresponding to the redirect URL. The redirect system may load thetarget landing page on the mobile device that scanned the static labelwithin ≤100 milliseconds from capturing a scan of the static label. Theredirect system may load a default webpage on the mobile device when aredirect URL is not received by the mobile device within 100milliseconds of scanning the static label. The default webpage mayinclude content accessible via the default URL encoded in the scannedstatic label.

The redirect system may track activity of a user on the target landingpage. The redirect system may track the activity based on capturinginputs of the user received by the mobile device. The redirect systemmay transmit the tracked activity to the redirect service. The targetlanding page may be a first target landing page. The redirect system mayload a second target landing page on the mobile device based on thetracked activity on the first landing page. Based on the trackedactivity, the redirect service may dynamically update, an intent indexscore, user profile and/or a multi-dimensional matrix associated. Ifactivity of the user on the target landing page is below a threshold,the redirect system may redirect another user that scans the staticlabel to a different landing page.

An artificial intelligence (“AI”) redirect system for dynamicallygenerating a customized landing page is provided. The redirect systemmay generate a customized landing page in response to a scan of amachine-readable label. The machine-readable label may be a staticlabel. The redirect system may include a processor and a non-transitorymemory with instructions stored thereon. The instructions when executedby the processor, cause the processor to redirect a mobile device totarget content formulated in response to the scan of the static label.The redirect system may be hosted on a computer system remote from adevice that captures the scan of the static label.

The redirect system may receive a default URL. The default URL may beextracted from a first scan of the static label. The redirect system mayreceive a first set of scan event details associated with the firstscan. The redirect system may generate a first redirect URL for thefirst scan based on the first set of scan event details. In response tothe first scan, the redirect system may trigger loading of first contentlinked to the first redirect URL. The first content may be loaded usinga browser on mobile device that performed the first scan.

The redirect system may receive the default URL extracted from a secondscan of the static label. The redirect system may receive a second setof scan event details associated with the second scan of the staticlabel. The redirect system may generate a second redirect URL for thesecond scan based on the second set of scan event details. The redirectsystem may trigger loading of second content linked to the secondredirect URL in response to the second scan. The second content may beloaded using a browser on mobile device that performed the second scan.

The redirect system may receive the first scan from a first mobiledevice. The redirect system may trigger the loading of the first contenton the first mobile device. The redirect system may receive the secondscan from a second mobile device. The redirect system may trigger theloading of the second content on the second mobile device.

The first content may be different from the second content. The firstcontent may be customized for the user of a first mobile device based onone or more first scan event details captured by the first mobiledevice. The first content may be customized for a first user of thefirst mobile device based a user profile of the first user. The secondcontent may be customized based on one or more second scan event detailscaptured by a second mobile device. The second content may be customizedfor a second user of the second mobile device based a user profile ofthe second user. The second content linked to the second redirect URLmay include a reordering of links presented in the first content linkedto the first redirect URL.

In response to receiving the first scan, the redirect system maytransmit tracking code, such as tracking pixel or cookie, to a mobiledevice that captured the first scan. The redirect system may generate anintent index score based and/or update a CIG on the first (historical)and second (current) sets of scan event details. The intent index scoreand/or CIG may be updated based on a third set of scan event detailscaptured by the tracking code embedded in the target page generatedbased on the first and second sets of scan event details. Illustrativetracking code may implement Federated Learning of Cohorts or othertracking methods that group users based on common activity associatedwith each of the users. Such tracking code may track event details suchas time of day, day of week, day of month, type of mobile device, typeof operating system used to scan, threshold number of apps running on,or resident in, the mobile devices, mobile service providers, geographiclocation, location of employment, or any other suitable scan eventdetails.

The redirect system may receive a third scan of a second static label.In response to receiving the third scan, the redirect system maygenerate a customized landing page for the mobile device in response tothe third scan. The redirect system may generate the customized landingpage in response to the third scan based on the intent index scoreand/or CIG. The redirect system may generate a third redirect URL forthe customized landing page. The redirect system may trigger loading ofthe customized landing page by transmitting the third redirect URL tothe mobile device that captured the third scan.

Apparatus may include a software application. The software applicationmay be run on a mobile device. The software application may be used toactivate a machine-readable label scanned by the mobile device.

An illustrative activation process may begin when the mobile device isused to scan a label. The label may be associated with an activationcode. The activation code may be presented in packaging associated withthe label. For example, the label may be printed on a sticker. Thepackaging of the sticker may include the activation code.

The software application may detect that the label has been scanned bythe mobile device. The software application may submit informationencoded in the label to a redirect service. The redirect service maydetect that the label has been scanned for the first time. The redirectservice may detect that the label is currently inactive. An inactivelabel may not be associated with content other than content at a defaultURL encoded in the label. The redirect service may detect that the labelhas not yet been linked to one or more target landing pages.

The redirect service may provide instructions to the softwareapplication that prompts the user of the mobile device to enter theactivation code packaged with the label. The redirect service maydetermine that the label has not been activated and provide instructionsto the software application in real-time. Real time may be defined as≤100 milliseconds from a time the mobile device scanned the label. Adelay of greater than 100 milliseconds after scanning the label may benoticeable by a human user.

The software application may also capture scan event details. Forexample, the software application may detect a scan location (orlocation area) based on a GPS location of the scanning mobile device,signal triangulation associated with the scanning mobile device, orlatitude/longitude coordinates as detected by the mobile device. Thesoftware application may map a detected location to a particular streetaddress. As part of the activation process, the software application mayprompt the user to confirm the detected street address or location area.

The software application may prompt the user to confirm a placementlocation of the label. The software application may prompt the user foradditional address details such as a floor or suite number. The softwareapplication may prompt the user to capture a picture of the labelaffixed to the placement location. The placement location may be amoveable object such as a bicycle, car, shirt or hat. The placementlocation may be a virtual location such as within a video stream, withina content of a TV show, on an electronic billboard, within AR content,VR content or other forms of electronic media. The software applicationmay submit the user inputs associated with label placement to theredirect service.

The redirect service may save the inputs received from the user in themulti-dimensional matrix. The redirect service may determine content toassociate with the scanned label. The redirect service may determinecontent to associate with the label based on scan event details capturedduring activation of the label. The redirect service may determinecontent to associate with the label based on inputs received from theuser during activation of the label. The redirect service may determinecontent to associate with the label based on applying one or moremachine learning algorithms to the multi-dimensional matrix.

After activation, subsequent scans of the label may be redirected, bythe redirect service, to the content associated with the label. Afteractivation, the redirect service may dynamically determine content todisplay in response to a scan of the label by a mobile device.

The redirect service may be utilized to reprogram a static label. Anexemplary static label may be a printed label affixed to a tangiblemedium. For example, a static label may be printed on a physical card oraffixed to real property. The redirect service may reprogram the staticlabel such that the static label is only active when scanned inside apre-defined geo-fenced area.

For example, a static label printed on promotional materials distributedat a convention may link to first content for a duration of theconvention when scanning within a location (e.g., building) where theconvention is being held. After a conclusion of the conference, a scanof the static label may link to information about an upcoming event orinformation about the conference organizer or sponsor.

Within the convention location, the static label may link to differentcontent depending on where the static label is scanned. When a userscans the static label in a cafeteria, a menu may be displayed. Thedisplayed menu may be dependent on a time the label is scanned, the dayof the week that the label is scanned, or the day of the month that thelabel is scanned. For example, a lunch menu may be displayed in responseto a scan captured between 11 am and 3 pm. A dinner menu may bedisplayed in response to a scan captured between 4 μm and 9 pm. When auser scans the static label in a conference room, a schedule or agendaassociated with an upcoming event to be held in that conference room maybe displayed in response to the scan.

A static label may be programed to be associated with any suitablecontent. A static label affixed to a stadium may be programmed topresent content associated with an ongoing event at the stadium. Astatic label may be programmed to be associated with a targetfunctionality. For example, the static label may be programmed to, inresponse to a scan, initiate a purchase from a merchant. The staticlabel may be programmed to initiate a purchase limited to a maximumvalue.

The static label may be programmed to only display content when thelabel is affixed to a target item, such as on apparel, a car or realproperty. A target item may include a beacon that broadcasts itslocation. The scanning device may include a location sensor. Location ofthe scanning device may be correlated to a location of a scanned label.

Static labels may be preprinted and encoded with fixed information. Theredirect service may be utilized to redirect the scanning device to anysuitable content. A static label may be reprogrammed by the redirectservice to change content associated with the static label. Contentassociated with a static label may be changed by the redirect servicewithout reprinting the static label to include different encodedinformation. The redirect service may require an activation process forthe static label before redirecting to suitable content. The redirectservice may be utilized to redirect a scanning device to any suitablecontent based on one or more scan event details (e.g., location, time,day of week, day of month, weather, scanning device, type of scanningdevice) associated with a scan of the static label.

A static label may include an embedded NFC chip. The NFC chip mayinclude functionality for transmitting information encoded on the labelto a receiving device. NFC transmissions may have a limited range ofabout 10 centimeters.

The NFC chip embedded in the label may receive information from ascanning device. The embedded NFC chip may store scan event details. Thestored scan event details may be extracted by an instance of theredirect service running on another scanning device that scans thelabel. The redirect service may formulate content for the other scanningdevice based on the scan event details stored in the embedded NFC chip.

Information received from the embedded NFC chip may be submitted to aredirect service to determine content associated with the label. A labelthat includes an embedded NFC chip may present first content to adevice. The label can preferably communicate with the label using NFC.The label may present second content to a device that scans the labelusing a camera.

Methods may include combining “public” information—i.e., informationretrieved from a public-facing label—with “private” information—i.e.,information derived from a scanning device (and/or scanner user). Such acombination of information may determine one or more actions takensubsequent to a scan. For example, a scan of a label captured by apre-registered scanning device may be associated with access to specialcontent such as purchase discounts, special offers or informationdesigned for employees. The redirect service may respond to a scanreceived from a pre-registered scanning device, or type of scanningdevice, with a target landing page that includes content formulatedspecially for the pre-registered scanning device or user of thepre-registered scanning device.

A label may be programmed to display specific content when scanned by apre-registered scanner. For example, a pre-registered scanning devicemay submit scan event details to the redirect service that includesidentifying information about the scanning device itself. Scanneridentifying information may include a MAC address or IP address of thescanning device. Based on the received identifying information, theredirect service may confirm that the scanning device is apre-registered scanning device. The redirect service may provide thepre-registered scanning device with content associated with the scanninglabel that is not provided to non-registered scanning devices.

Apparatus and methods in accordance with this disclosure will now bedescribed in connection with the figures, which form a part hereof. Thefigures show illustrative features of apparatus and method steps inaccordance with the principles of this disclosure. It is to beunderstood that other embodiments may be utilized, and that structural,functional and procedural modifications may be made without departingfrom the scope and spirit of the present disclosure.

The steps of methods may be performed in an order other than the ordershown and/or described herein. Method embodiments may omit steps shownand/or described in connection with illustrative methods. Methodembodiments may include steps that are neither shown nor described inconnection with illustrative methods. Illustrative method steps may becombined. For example, an illustrative method may include steps shown inconnection with any other illustrative method and/or apparatus.

Apparatus may omit features shown and/or described in connection withillustrative apparatus. Apparatus embodiments may include features thatare neither shown nor described in connection with illustrativeapparatus. Features of illustrative apparatus may be combined. Forexample, an illustrative apparatus embodiment may include features shownor described in connection with any other illustrative apparatus and/ormethod embodiment.

In some embodiments, a user with a mobile phone may initiate aninteraction with a label by using the camera on the mobile phone to scanmachine-readable optical label 100 illustrated in FIG. 1. Label 100 mayencode any suitable information such as a Uniform Resource Locator(“URL”), contact information associated with a business or otheralphanumeric information and instructions that trigger the scanningdevice to perform a target action or function. The information encodedin label 100 may be extracted by a native application or a third-partyapplication running on a scanning device.

Label 100 illustrates an encoded pattern that conforms to an encodingspecification for a Quick Response (“QR”) code. Machine-readable labelsdescribed in connection with this disclosure may be any suitablescannable label. In other examples, the machine-readable label may be alinear barcode or a two-dimensional matrix barcode, e.g., Aztec code,ShotCode, SPARQCode, and the like.

Label 100 may be a “static” machine-readable label. A “static” label mayencode a set of instructions that are repeatedly scanned by multipleusers and/or mobile devices. Even though label 100 may be static, theparticular content that is presented to a user in response to a scan oflabel 100 is dynamically customized for each user.

FIG. 2 is a flowchart of exemplary process 200 for generatinginteractive customized content in response to a scan of a static label.Process 200 starts at step 217 when a user scans a machine-readablelabel such as label 100 (shown in FIG. 1) using camera 201. This isreferred to as a “scan event” in the present disclosure.

After the mobile phone recognizes the scanned label, it opens browser203 using a URL encoded in the label. For example, a URL encoded in thelabel may be a shortened URL (e.g., based on a URL shortening servicelike Bitly).

The URL encoded in the label is a redirect URL, and at step 221, it isloaded by browser 203. At step 223, redirect service 205 retrieves alanding page URL for the scanned label. If a unique cookie or otheridentifier does not exist for the user (or was sent by the browser), atstep 225, tracking code, such as a cookie or pixel is generated. At step227, scan event details and the tracking code (which may be configuredto track user browsing and online purchasing histories) are transmittedto a back-end database (e.g., data lake 213 that stores data in its rawor native format) for analysis. The back-end database may storeinformation received from redirect service 205 in a multi-dimensionalmatrix.

A landing page URL, which corresponds to target content generated forthe user in connection with scan event 217 is then computed by redirectservice 205. At step 229, redirect service 205 transmits the landingpage URL to browser 203, thereby redirecting browser 203 to the targetcontent. In some embodiments, the landing page URL is determined basedon user preferences, CIG, intent index scores, demographics, userprofile or other data stored in the multi-dimensional matrix. At step231, browser 203 receives the landing page URL and loads a targetlanding page target that includes content generated by redirect service205 for the user.

Redirect service 205 may interact with brand site 207. To customizecontent in response to a scan event, redirect service 205 may pullcontent from brand site 207. Brand site 207 may be linked within amulti-dimensional matrix to a redirect URL encoded in a scanned label.Step 239 shows that redirect service 205 may direct browser 203 to atarget landing page that is hosted by brand site 207. Step 239 showsthat redirect service 205 may direct browser 203 to a target landingpage that includes tracking code for capturing user actions on thetarget landing page.

At step 233, browser 203 loads a target landing page corresponding tothe landing page URL provided by redirect service 205. Redirect service205 may transmit tracking code, such as a tracking pixel or cookie tobrowser 203. Step 235 shows that when browser 203 loads the targetlanding page, a pixel may be embedded in the target landing page. Step237 shows redirect service 205 may commission third-party analytics 211.Third-party analytics 211 may also be used to track user activity on thetarget landing page or other webpages. Third-party analytics 211 mayalso be used to evaluate user activity on the target landing page orother webpages.

Step 241 shows that tracking code service 209 may receive tracking datafrom the tracking code embedded in the target landing page presented inbrowser 203. Tracking code service 209 may be a subsystem of redirectservice 205. At step 249, tracking data received at step 241 may bestored in data lake 213. Tracking data received at step 241 may bestored in a multi-dimensional matrix within data lake 213. Tracking datareceived at step 241 and stored in data lake 213 may be utilized byredirect service 205 to formulate content in response to a future scanevent.

At step 243, processing results of third-party analytics 211 aregenerated. At step 253, ETL (“Extract, Transform and Load”) service 215loads the processing results of third-party analytics 211 into data lake213. ETL service 215 may collect data from various sources and integratethe collected data into a single, centralized location (e.g., data lake213). At steps 251 and 253, ETL service 215 collects processing resultsof third-party analytics 211 or data from any other source andintegrates the collected data into data lake 213. Information integratedinto data lake 213 by ETL service 215 may be utilized by redirectservice 205 to formulate content in response to a scan event.

At step 247, data captured by third-party analytics 211 may beprocessed. Processing data captured by third party analytics 211 mayinclude determining a level of engagement and interaction with contentgenerated by redirect service 205 in response to a scan event.Third-party analytics 211 may determine whether user engagement orinteraction with the content generated by redirect service 205 hasexceeded a threshold level. At step 245, processing results ofthird-party analytics 211 is stored. The processing results may bestored in data lake 213.

In some embodiments, the steps from the scan event to browser 203loading a target landing page for the user can be represented asfollows:

-   -   1. Request made to https://flowto.it/:qr_id    -   2. Collect qr_id (or other user or scanning device identifying        information)    -   3. Look for tacking code (e.g., cookie)        -   a. If there save and use to reset the tracking code with new            TTL        -   b. If not, generate uid and use for tracking code    -   4. Look up qr_id in redirect service to get target landing page    -   5. Set tracking code in response    -   6. Send tracking code, qr_id and http request information to        data lake for analysis    -   7. Redirect user to target landing page

Operation (1) includes extracting a URL from a scanned label. The URLencoded in the scanned label is a redirect URL, which can be a shortenedURL. Operation (1) further includes launching, based on the redirectURL, a redirect service that is used to determine a target landing pagefor the use. The target landing page may be determined based on a qr_idfield, which uniquely identifies a user or scanning device. The qr_idfield may be a MAC address of the scanning device or a biometric featureof the user.

Operation (2) includes retrieving the qr_id field from the redirectservice. Operation (3) includes determining whether a tracking code forthe user associated with the (unique identifier uid) and the scannedlabel exists. If the tracking code exists, its time-to-live (“TTL”)field is reset since it has just been accessed. If the tracking codedoes not exist, it is generated along with a pseudo-randomly generateduniversally unique identifier (uid) used in connection with the trackingcode.

Operation (4) includes accessing a redirect service (redis) using theqr_id to retrieve, for example, the URL of a target landing page. Forexample, the redirect service may include an in-memory key-valuedatabase that can be configured to store a variety of different datastructures. Thus, the qr_id can be used to retrieve other information(e.g., special offers that have been curated for the user, other landingpage suggestions) in addition to a target landing page.

Operation (5) includes updating the tracking code based on theinformation retrieved in (4). Operation (6) includes sending the updatedtracking code and retrieved information to a third-party analyticservice in order to update the underlying machine learning models forgenerating content for the user or the scanned label. Operation (7)redirects the user to a target landing page generated by the redirectservice. The process of the user scanning a label with a mobile phonecamera (the scan event) and interacting with the presented targetlanding page, with the corresponding timestamps, may be referred toherein as a “touchpoint.”

In some embodiments, third-party web analytic services 211 can beincorporated into the process of providing a target landing page inresponse to a scan event. Third-party analytic services (e.g., GoogleAnalytics, Federated Learning of Cohorts) may provide measurement,collection, analysis and reporting of web traffic, and generate keyperformance indicators (“KPI”) that can be used to determine contentthat would be best suited to maximize user interaction time with thecontent generated in response to a scan event. Previous and ongoing userinteractions can be used to inform content generated in response tofuture scan events.

FIG. 3A illustrates exemplary content and services that may be presentedby a redirect service in response to a scan event. FIG. 3A illustratesthat different content and services may be presented in response to ascan of a static label (e.g., label 100) at different times by a singleuser. In the context of FIG. 3A, the processes described in FIG. 2 maybe performed on behalf of the same user at each of scan times t₁, t₂,t₃, t₄, . . . , t_(N) to obtain customized content in response to eachscan. Each of these scan times is a touchpoint that includes a userinteracting with the content computed by a redirect service in responseto a scan event.

The content generated by the redirect service at t₂ may be based on userinteractions with content presented at t₁. The content generated by theredirect service at t₃ may be based on user interactions with contentpresented at t₁ and user interactions with content presented at t₂. Moregenerally, content generated by the redirect service at t₁ may be basedon user interactions with content presented at t_(i-1), t_(i-2), t_(i-3). . . t_(1-N). User interactions with presented content may be obtainedfrom tracking code embedded in presented content, tracking code residenton a scanning device or third-party tracking and analytics.

As illustrated in FIG. 3A, the different content and services may bepresented to the user via a target landing page (e.g., as performed inoperation (7) described above). Illustrative content and servicesinclude presenting webpage 301 (e.g., created with HTML or JavaScript),initiating video conference services 302, initiating SMS or emailservices 303, initiating telephony services 304, initiating chatbot orvirtual assistant services 305, provisioning access to electronicdocument 306, presenting virtual reality (VR) or augmented reality (AR)content 307, opening social media portals (e.g., Twitter, Facebook,Snapchat, Instagram, etc.) 309 and initiating other services and/orframeworks 311.

FIG. 3B illustrates alternative exemplary content and services that maybe presented by a redirect service in response to a scan event. FIG. 3Balso illustrates that different content and services may be presented inresponse to a scan of a static label (e.g., label 100) at differenttimes by a single user. In the context of FIG. 3B, the processesdescribed in FIG. 2 may be performed on behalf of the same user (or inalternative embodiments a plurality of users) at each of scan daysMonday 313, Tuesday 315, Wednesday 317, Thursday 319, Friday 321, . . .etc., to obtain customized content in response to each scan on aspecific day. Each of these scan days can be considered a touchpointthat includes a user or group of users interacting with the contentcomputed by a redirect service in response to a scan event.

The content generated by the redirect service on Tuesday may be based onuser interactions with content presented on Monday. The contentgenerated by the redirect service on Wednesday may be based on userinteractions with content presented on Monday and user interactions withcontent presented on Tuesday. More generally, content generated by theredirect service on any given day may be based on user interactions withcontent presented on another day such as one day before the given day,two days before the given day, . . . , etc., or based on a combinationof user interactions over multiple days.

Similar to the content illustrated in FIG. 3A, illustrative content andservices in FIG. 3B include presenting webpage 301 (e.g., created withHTML or JavaScript), initiating video conference services 302,initiating SMS or email services 303, initiating telephony services 304,initiating chatbot or virtual assistant services 305, provisioningaccess to electronic document(s) 306, presenting virtual reality (VR) oraugmented reality (AR) content 307, opening social media portals (e.g.,Twitter, Facebook, Snapchat, Instagram, etc.) 309 and initiating otherservices and/or frameworks 311.

FIG. 4A illustrates an example of different content and services thatmay be presented by a redirect service in response to different usersscanning instances of a static label (e.g., label 100) at a single time.FIG. 4A illustrates the process described in FIG. 2 being performed foreach of users 401 (User1), 403 (User2), 405 (User3), 407 (User4), . . ., 409 (UserN) in response to scanning label 100 at t₁. Each scan oflabel 100 by a different user may be a discrete scan event. Content foreach scan event may be formulated by a redirect service based on scanevent details associated with the scan event.

As illustrated in FIG. 4A, the different content and services that maybe presented by a redirect service (e.g., as performed in operation (7)described above) include presenting webpage 301 (e.g., created with HTMLor JavaScript), initiating video conference services 302, initiating SMSor email services 303, initiating telephony services 304, initiatingchatbot or virtual assistant services 305, provisioning access toelectronic document(s) 306, presenting virtual reality (VR) or augmentedreality (AR) content 307, opening social media portals (e.g., Twitter,Facebook, Snapchat, Instagram, etc.) 309 and initiating other servicesand/or frameworks 311.

FIG. 4B illustrates an example of different content and services thatmay be presented by a redirect service in response to different usersscanning instances of a static label (e.g., label 100) using a singleOperating System (“OS”)/and/or/Device Type. Exemplary OS and devicetypes may include iOS 14.5 running on Apple 14, and Android 11.0 runningon a Pixel Smartphone.

FIG. 4B illustrates the process described in FIG. 2 being performed foreach of users 411 (User1—First OS/device type), 413 (User2—FirstOS/device type), 415 (User3—First OS/device type), 417 (User4—FirstOS/device type), . . . , 419 (UserN—N OS/device type) in response toscanning label 100 at t₁. Each scan of label 100 by a different userusing a different OS/device type may be a discrete scan event. Contentfor each scan event may be formulated by a redirect service based onscan event details associated with the scan event. Accordingly, eachuser may be provided different, possibly customized, content based onthe user's device type.

As illustrated in FIG. 4B, the different content and services that maybe presented by a redirect service (e.g., as performed in operation (7)described above) include presenting webpage 301 (e.g., created with HTMLor JavaScript), initiating video conference services 302, initiating SMSor email services 303, initiating telephony services 304, initiatingchatbot or virtual assistant services 305, provisioning access toelectronic document(s) 306, presenting virtual reality (VR) or augmentedreality (AR) content 307, opening social media portals (e.g., Twitter,Facebook, Snapchat, Instagram, etc.) 309 and initiating other servicesand/or frameworks 311.

In FIGS. 3A-3B and 4A-4B, specific content and services that may bepresented to the user within a target landing page, may be formulated bya redirect service based on a variety of attributes. The variety ofattributes may include scan event details associated with each scanevent. The variety of attributes may include scan event detailsassociated with previous scan events.

For example, FIGS. 3A-3B may include a scenario of a user walkingthrough a department store or mall and scanning labels at differentlocations and at different times or different days, respectively. Inresponse to a scan event at a target location, the redirect service mayformulate a landing page that is presented content related to thespecific product within a threshold distance of a scanned label. Inresponse to a scan event (e.g., at time t₄), a scan event day of week,and/or a scan event day of month, the redirect service may present to auser discount for a product based on similar products the user has showninterest in at earlier scan events (e.g., at times t₁ and t₂ or daysMonday and Tuesday).

As a further example, in FIG. 4A a subset of users (e.g., User 1, User 2and User4) may be located in the same geographic region and may eachscan a label to access information about eating out. The redirectservice may formulate a landing page for User 1 and User 4 thatincentivizes User 1 and User 4 to dine together at the same eatery ifthe redirect service determines that User 1 and User 4 know each other(e.g., based on social media feeds). To incentivize User 1 and User 4 todine together at the same eatery, the redirect service may formulate adiscount at a nearby target restaurant for both User 1 and User 4.

FIG. 5 shows illustrative feedback loop 511. Feedback loop 511 may beutilized to dynamically formulate content presented in a target landingpage by a redirect service. Feedback loop 511 shows that a redirectservice (e.g., utilizing machine learning algorithms) may account forprevious scan events and content generated in response to those previousscan events when computing content for a current scan event.

As illustrated in FIG. 5, after a user has interacted with a targetlanding page, user activity on the landing page (user_group_x) 503 andlanding page information (landing_page_y) 501 is input into redirectservice 205. Redirect service 205 may update user group and landing pagecorrelations and output score 507 (score_xy). Score 507 may represent alevel of user group engagement 509 of the user with the content onlanding page (landing_page_y) 501. Score 507 may be an intent indexscore. It should be noted that a user may be part of a group by virtueof sharing one or more aspects of a user profile with other users.Profiles may be considered similar for different reasons. These reasonsmay include levels of user interaction, device type, etc.

Score 507 and/or user group engagement 509 may be used by a redirectservice to formulate content for landing pages presented to the user orusers in response to future scan events. For example, a landing page mayinclude a plurality of links. Based on links accessed by the user, thelinks may be reordered the next time the user scans a label that isassociated with the plurality of links. It should be noted that such auser group engagement 509 may include forming a user group fromdisparate users based on the tendencies of the users. Such a groupengagement 509 may include forming a user group from one or more metricsassociated with the group. Such a group engagement 509 may includeforming a user group from one or more scan devices details. Such a groupengagement 509 may include forming a user group from any one or moreuser-relevant details.

Redirect service 205 may apply one or more machine learning algorithmsthat provide content recommendations. The machine learning algorithmsmay provide content-agnostic recommendations. For example, redirectservice 205 may extract patterns in scan events, scan event details,user activity on target landing pages or any online or offline useractivity to formulate content recommendations. For example, based onscan events or scan event details, redirect service 205 may determinethat 90% of users show interest in two items. Redirect service 205 mayrecommend content associated with the second item to a user who scans alabel associated with the first item.

Redirect service 205 may include a user-user framework or an item-itemframework. In the former, content recommendations provided to aparticular user are based on finding users that are similar to thatparticular user, and then recommending items liked by those similarusers. Whereas, in the latter, items that are similar to items liked bythe user are identified and recommended. User similarity may bedetermined based on two or more users sharing a threshold number ofattributes in each of their user profiles. Item similarity may bedetermined based on similar users expressing interest in two or moreitems. Item similarity may be determined based on attributes of theitems, such as cost, functionality, availability, manufacturers,reseller or any suitable attribute associated with an item.

Thus, redirect service 205 may utilize the user-user framework tosuggest particular content to a user based on one more users havingengaged with the particular content (e.g., a social media platform issuggested to a user because their user profile shares a threshold numberof attributes with social media influencers). Similarly, content can besuggested to a user based on the engagement of that user with otherrelated content (e.g., an AR experience may be loaded for a user thathas previously spent a long time engaging with VR frameworks in the casethat the brand the user is engaging with does not have a VR frameworkavailable).

Embodiments of the disclosed technology employ a user profile (or userpersona) that consist of multiple attributes representative of a user.The attributes may include user preferences (e.g., favorite brands,favorite movies or TV shows, etc.), previous purchasing and browsingactivity of the user, scan event details and user demographicinformation (e.g., gender, age, marital status, income, etc.).

The user profile may include a CIG that connects the user's offlineactivity to the user's online activity. The CIG may link offline scanevent details (such a label location, scan location, scan time, weatherat scan time, physiological information of the user) to online useractivity in response to a scan event. Illustrative online activity mayinclude landing pages presented to the user in response to scan events,online activity on the user on the presented landing pages, previousbrowsing and purchasing history of the user (e.g., which websites oronline chatrooms has the user frequented in the last 30, 60 or 90 days).

Redirect service 205 correlate attributes, and based on thecorrelations, determine a target landing page for the user in responseto a scan event. Furthermore, the user profile may be dynamicallyupdated based on activity (online or offline) in response to a currentscan event (or activity associated with the most recent N scan events).Score 507 may be used to determine utility of content to a user or userprofile in response to a scan event. A high score may correspond tocontent likely having a high level of interest to the user.

In some embodiments, score 507 computed in FIG. 5 is based on a seriesof individual scores, each of which quantify a correlation between oneattribute in the user profile (or user persona) and content or services.Individual scores may be generated more generally for a universe ofusers based on attributes stored in the multi-dimensional matrix,representing multiple user profiles and different content and services.Score 507 may be generated based on scan event details, such as scanlocation (e.g., home, office public location), time of scan, weather attime of scan, physiological information of the user (e.g., at the scantime) and/or demographic information associated with the user.Illustrative physiological characteristics may include heart rate, bodytemperature, body motion, facial expression, speech patterns orbiometric features.

Illustrative computational techniques that may be used to implementfeedback loop 511 include application of machine learning techniques,such as AdaBoost, Naive Bayes, Support Vector Machine, Random Forests,Artificial Neural Networks and Convolutional Neural Networks.

FIG. 6 illustrates an illustrative Long Short-Term Memory (“LSTM”) unit600, which may be utilized by redirect service 205. For example, LSTMunit may be used to construct a deep neural network for implementingfeedback loop 511 or otherwise generating content in response to a scanevent. Deep neural networks that use an LSTM architecture are wellsuited to classifying, processing and making predictions based on timeseries data (e.g., a time-series of “touchpoints”), since there can belags of unknown duration between events in a time series. As illustratedin FIG. 6, LSTM unit 600 is composed of cell 603, input gate 601, outputgate 605 and forget gate 609. LSTM unit 600 generates predicted segment613 based on input 611. Input 611 may include time-series of user cohorttouchpoints.

In the context of FIG. 6, cell 603 is responsible for keeping track ofthe dependencies between the elements in input sequence 611. Input gate601 controls the extent to which a new value flows into cell 603. Forgetgate 609 controls the extent to which a value remains in cell 603. Aforgetting factor associated with forget gate 609 may be used to controlthe extent to which a value remains in cell 603. The forgetting factormay be updated to account for changes in user browsing and purchasingbehaviors. Output gate 605 controls the extent to which the value incell 603 is used to compute predicted segment 613. Predicted segment 613may include content formulated in response to a scan event.

In some embodiments, input 611 to the DNN is a series of touchpointsassociated with a particular user. The DNN uses input 611 and attributesof a user profile to generate predicted segment 613. Predicted segment613 may include a target landing page for that user at that time or inresponse to any combination of scan event details. The DNN is alsoconfigured to update a user profile based on predicted segment 613. TheDNN may update the user profile using feedback loop 511 shown in FIG. 5.

In some embodiments, a gated recurrent unit (“GRU”) (not shown) may beused instead of or in conjunction with LSTM unit 600. More generally,using a GRU, a deep neural network (“DNN”) may be configured to operateas a recurrent neural network (“RNN”), in which the connections betweennodes form a directed graph along a temporal sequence, andadvantageously enables it to exhibit dynamic temporal behavior.

In some embodiments, a transformer model (not shown) may be used insteadof or in conjunction with LSTM unit 600 or a GRU. Transformer modelsutilize context within data to assess the data. As opposed to LSTM unitsor GRUs, transformer model may process larger quantities of inputsimultaneously (to extract contextual data).

FIG. 7 shows illustrative hardware architecture 700 that may be used toimplement apparatus and methods disclosed herein. Hardware architecture700 includes processor 702. Processor 702 is in communication withmemory unit 704 and input/output (I/O) unit 706. Processor 702 may beconfigured to process data. Memory unit 704 may store and/or buffer thedata generated by processor 702. To support various functions ofhardware architecture 700, processor 702 may interface with and control(e.g., via the I/O unit 706) operations of other devices.

Processor 702 may include one or more processors, e.g., including butnot limited to microprocessors such as a central processing unit(“CPU”), microcontrollers, or the like. Memory unit 704 may include andstore processor-executable code, which when executed by processor 702,configures hardware architecture 700 to perform various operations,e.g., such as receiving information, commands, and/or data, processinginformation and data, and transmitting or providing information/data toanother device. Memory unit 704 may store other information and data,such as instructions, software, values, images, and other data processedor referenced by processor 702. For example, memory unit 704 may includeRandom Access Memory (“RAM”), Read Only Memory (“ROM”), Flash Memory,and other suitable storage media to implement storage functions.

In some implementations, hardware architecture 700 includes input/outputunit (I/O) 706 to interface processor 702 and/or memory unit 704 toother modules, units or devices associated with hardware architecture700, and/or external devices. For example, I/O unit 706 can connect toan external interface, source of data storage, or display device.Various types of wired or wireless interfaces compatible with typicaldata communication standards, such as Universal Serial Bus (“USB”), IEEE1394 (FireWire), Bluetooth, Bluetooth low energy (“BLE”), ZigBee, IEEE802.11, Wireless Local Area Network (“WLAN”), Wireless Personal AreaNetwork (“WPAN”), Wireless Wide Area Network (WWAN), WiMAX, IEEE 802.16(Worldwide Interoperability for Microwave Access (WiMAX)), 3G/4G/LTE/5Gcellular communication methods, and parallel interfaces, can be used tocommunicate data with the device via I/O unit 706.

For example, hardware architecture 700 may include a wirelesscommunications unit, e.g., such as a transmitter (Tx) or atransmitter/receiver (Tx/Rx) unit. In such implementations, I/O unit 706can interface processor 702 and memory unit 704 with the wirelesscommunications unit to utilize various types of wireless interfaces,such as the examples described above.

I/O unit 706 can interface with other external interfaces, sources ofdata storage, and/or visual or audio display devices, etc. to retrieveand transfer data and information that can be processed by processor702, stored in memory unit 704, or exhibited on an output unit of a userdevice (e.g., display screen of a computing device) or an externaldevice. I/O unit 706 may include a touchscreen. I/O unit 706 may includea camera.

FIG. 8 shows illustrative process 800 for generating customized contentin response to a scan event. Process 800 may be performed by systemsshown in FIGS. 2 and 5-7. Process 800 includes, at step 810, receiving,from the user or cohort, a scan of a machine-readable optical labelcaptured using a camera of a mobile device.

Process 800 includes, at step 820, determining, based on the scan(s), aredirect Uniform Resource Locator (URL) encoded in the scanned label.Process 800 includes, at step 830, determining, based on a user (orcohort) profile and a redirect service that is accessed using theredirect URL, a landing page URL. Process 800 includes, at step 840,providing, to the user through a browser on the mobile device, a landingpage, based on the landing page URL, comprising content customized forthe user.

Implementations of the subject matter and the functional operationsdescribed in this disclosure may be implemented in various systems,digital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this disclosure andtheir structural equivalents, or in combinations of one or more of them.Implementations of the subject matter described in this disclosure canbe implemented as one or more computer program products, e.g., one ormore modules of computer program instructions encoded on a tangible andnon-transitory computer readable medium for execution by, or to controlthe operation of, data processing apparatus. The computer readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, a composition of matter effecting amachine-readable propagated signal, or a combination of one or more ofthem. The term “data processing unit” or “data processing apparatus”encompasses all apparatus, devices, and machines for processing data,including by way of example a programmable processor, a computer, ormultiple processors or computers. The apparatus can include, in additionto hardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit). In an example, a DNN may beimplemented on an ASIC or FPGA.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random-access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Computer readable media suitable for storingcomputer program instructions and data include all forms of nonvolatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

FIG. 9 shows illustrative process 900. Process 900 begins at to whenusers 908, 909 and 910 (hereinafter “users”) scan label 901 using mobiledevices 911, 912 and 914 (hereinafter “devices”). Label 901 ispositioned in store window 906 of pizza restaurant 907. Label 901includes an encoded redirect URL. The encoded URL, when scanned bydevices 911, 912, 914, triggers mobile devices 911, 912, 914 to submitthe redirect URL to redirect service 913. Redirect service 913 mayinclude one or more features of redirect service 205 (shown in FIGS. 2and 5).

At t₁, redirect service 913 receives the redirect URL from mobiledevices 911, 912, 914. In response to receiving the redirect URL,redirect service 913 may employ artificial intelligence, such as a DNNor one or more machine learning algorithms to formulate content thatwill be presented on mobile devices 911, 912, 914 in response toscanning label 901. To formulate the content, redirect service 913 mayutilize CIG 903 to compute an intent index score for the scan event atto. Redirect service 913 may compute the intent index score based onscan event details 905. It should be noted that users in a cohort canpreferably receive relevant, preferably customized, content at leastbecause they share one or more metrics that groups them into the cohort.

FIG. 9 shows that at t₂, based on CIG 903 and scan event details 905,redirect service 913 has computed an intent index score of 93/100(relatively high) with respect to users 908, 909, 910 taking actionregarding an available “dine-in pizza” offer 917 associated with scannedlabel 901. Redirect service 913 may compute, based on CIG 903 and scanevent details 905, that user 909 has an intent index score of 27/100(relatively low) with respect to taking action regarding an available“deliver pizza” offer 921.

FIG. 9 shows that at t₃, content 915, is presented on mobile devices911, 912, 914. Content 915 includes “dine-in pizza” offer 917. Redirectservice 913 may formulate content 915 and trigger presentation ofcontent 915 on mobile devices 911, 912, 914 in real-time. Real-time maybe defined as a total duration of ≤100 milliseconds between t₀ and t₅.

FIG. 10 shows illustrative computational process 1000. Computationalprocess 1000 may be executed by redirect service 913 (shown in FIG. 9).At step 1001, redirect service 913 activates label 901. For example, aproprietor of pizza restaurant 907 may affix label 901 to store window906. The proprietor may activate label 901 in connection with potentialoffers 917 and 921.

An illustrative activation process may include the proprietor scanninglabel 901. Label 901 may be associated with an activation code (notshown). The activation code may be presented on packaging associatedwith label 901. For example, label 901 may be printed on a sticker. Thepackaging of the sticker (e.g., removeable backing) may include theactivation code.

Label 901 may encode a redirect URL. Scanning the redirect URL maytrigger the scanning device to submit scan event details including theredirect URL to redirect service 913. In response to receiving the scanevent details, redirect service 913 may determine that non-activatedlabel 901 has been scanned. Redirect service 913 may determine thatlabel 901 has not yet been linked to one or more landing pages or othercontent/service.

Redirect service 913 (which may include a software application runningon the proprietor's mobile device) may request an activation code.Redirect service 913 may provide instructions to the mobile device ofthe proprietor to enter the activation code packaged with label 901. Theprompts may ask the proprietor to confirm a placement location of label901 in store window 906. Redirect service 913 may prompt the proprietorfor additional address details associated with store window 906 such asfloor or suite number. Redirect service 913 may prompt the proprietor tocapture a picture of label 901 affixed to store window 906.

During (or after) activation of label 901, the proprietor may submitoffers 917 and 921 to redirect service 913. The proprietor may requestredirect service 913 associated label 901 with offers 917 and 921. Theproprietor may request that redirect service 913 determine which ofoffers 917 and 921 to present in response to scans of label 901. Basedon scan event details, such as a mobile device identifier or biometrics,redirect service 913 may determine whether label 901 is being scanned bythe proprietor or a potential customer of pizza restaurant 907.

Redirect service 913 may save activation inputs received from theproprietor in a multi-dimensional matrix. Redirect service 913 may saveactivation inputs received from the proprietor in user profile for theproprietor. Redirect service 913 may formulate content to associate withlabel 901 based on the scan event details associated with a future scanof label 901. Redirect service may formulate content to associate withlabel 901 in response to a future scan by applying one or more machinelearning algorithms to attributes, such as the activation inputs, storedin the multi-dimensional matrix.

After activation step 1001, at step 1003, user 909 initiates a scan oflabel 901. Label 901, when scanned by mobile devices 911, 912, 914 ofusers 908, 909, 910, triggers mobile devices 911, 912, 914 to submit theredirect URL encoded in label 901 to redirect service 913. Step 1003also shows that users 908, 909, 910 are associated with CIG 903.Attributes (e.g., scan event details) included in CIG 903 may be indexedby a unique identifier (e.g., FlowID) assigned to users 908, 909, 910.CIG 903 may include one or more logical relationships among attributesstored in CIG 903. CIG 903 may correlate offline activity of users 908,909 910 (e.g., scanned label locations) to online activity of users 908,909, 910 (action taken in response to a scan event).

At step 1005, in response to receiving the redirect URL, redirectservice 913 may employ artificial intelligence, such as a DNN or one ormore machine learning algorithms to compute which of offers 917 and 921will be presented to users 908, 909, 910 on mobile devices 911, 912, 914in response to the scanning of label 901. To formulate the content,redirect service 913 may utilize relationships stored in CIG 903 tocompute an intent index score for the scan event. Redirect service 913may compute the intent index score based on scan event details 905captured when users 908, 909, 910 scans label 901.

In certain embodiments, the content may be formed in such a way as totake advantage of the characteristic(s) upon which the cohort was based.For example, if the cohort was formed based on the fact all users sharea specific mobile device manufacturer, then the content may include anoffer for a mobile device accessory specific to the manufacturer sharedby the cohort.

Step 1005 shows that based on CIG 903 and scan event details 905,redirect service 913 has computed an intent index score of 93/100(relatively high) with respect to “dine-in pizza” offer 917 associatedwith scanned label 901. Redirect service 913 may determine, based on CIG903 and scan event details 905, that users 908, 909, 910 has an intentindex score of 21/100 (relatively low) with respect to taking actionregarding an available “deliver pizza” offer 921.

FIG. 11 shows illustrative scenario 1100. Scenario 1100 shows that scanevent details 1101 may be captured in response to device 1107 scanninglabel 901. Scenario 1100 shows that based on scan event details 1101,content 1113 is presented on scanning device 1107. Scenario 1100 showsthat scan event details 1105 may be captured in response device 1109scanning label 901. Scenario 1100 shows that based on scan event details1105, content 1115 is presented on scanning device 1109.

Based on scan event details 1101 and attribute relationships stored inCIG 1103, redirect service 205 (shown in FIG. 2) may compute intentindex score 1117. Based on scan event details 1105 and attributerelationships stored in CIG 1103, redirect service 205 may computeintent index score 1119. CIG 1103 may include logical relationshipslinking offline activity of users to online activity of the users.Redirect service 205 may apply a machine learning algorithm, such as aDNN to scan event details 1101 and formulate content 1113 presented onscanning device 1107 in response to scan event #1. The redirect servicemay apply a machine learning algorithm, such as a DNN to scan eventdetails 1105 and formulate content 1115 presented on scanning device1109 in response to scan event #2. CIG 1103 may correlate scan eventdetails 1101 and 1105 to online activity of users after the users arepresented with content 1113 and 1115.

Redirect service 205 may apply machine learning algorithms to scan eventdetails 1101 and 1105 to generate CIG 1103. CIG 1103 may correlate scanevent details 1101 and 1105 to each other. CIG 1103 may correlate scanevent details 1101 and 1105 to each other and to user profiles or otherdata stored in data lake 213. CIG 1103 may link trackable electronicactivity of users (e.g., scan event details 1101 and/or 1105) to one ormore static machine-readable optical labels, such as label 901. CIG 1103may link trackable electronic activity of users to a staticmachine-readable optical positioned at a fixed location (e.g., label 901positioned in store window 906).

Redirect service 205 may utilize CIG 1103 to compute index scores (e.g.,1117 and 1119) for potential actions or content that may be presented toa user in response to a scan event. For example, scenario 1100 showsthat based on temperature (cold) and time (early morning), content 1113is presented on scanning device 1107. Scenario 1100 shows that based ontemperature (hot) and time (mid-morning), content 1115 is presented onscanning device 1109.

“FlowID” shown in FIG. 11 may represent a unique identifier that links auser profile to attributes within CIG 1103. In some embodiments, withinCIG 1103, a FlowID may represent a unique identifier that links amachine-readable label to activity of users after scanning the label.For example, CIG 1103 may correlate scan event details 1101 to scanevent details 1105 and then be utilized to formulate content in responseto future scans of label 901.

In some embodiments, a user interface and/or application programinterface (“API”) may provide access to redirect service 205. Access toredirect service 205 via an API may allow third-party applications tosubmit additional user attributes to redirect service 205. Theadditional attributes may be used to formulate or update CIG 1103.Access to redirect service 205 via the API may allow third-partyapplications to submit scan event details or other attributes andreceive content formulated by redirect service 205 based on CIG 1103.

FIG. 12 shows illustrative process flow 1200. Process flow 1200 beginswhen a user scans machine-readable optical label 1201. Informationencoded in label 1201 may trigger the scanning device to submit scanevent details to a redirect service. The redirect service may apply amachine learning algorithm to CIG 1103 and formulate content 1209 topresent in response to the scan of label 1201.

FIG. 12 shows that scan event details may include an interface used tocommunicate with a redirect service. For example, scan event details1205 indicate the scanning device is communicating with a redirectservice using a web-based communication flow. Scan event details 1207indicate the scanning device is communicating with redirect serviceusing a text message communication flow. Redirect service may formulatecontent 1209 to present in response to the scan of label 1201 based onscan event details 1207 or 1209.

For example, if scan event details 1207 are received, redirect servicemay determine that a user should be presented with offer 917. Theredirect service may respond with a text message that presents offer 917and allows the user to respond to the text message by “replying ‘yes’ toorder now.” If scan event details 1207 are received, redirect servicemay determine that a user should be presented with offer 921. Theredirect service may respond by opening a webpage that presents a menufor pizza restaurant 907.

FIGS. 13-15 shows illustrative machine-readable labels that may bescanned during a scan event. FIGS. 13-15 show illustrative static labelsthat may each have a different appearance and encode identicalinformation. For example, each of the labels shown in FIGS. 13-15 mayencode the same redirect URL. Despite differences in appearance, each ofthe labels shown in FIGS. 13-15 may be the same static label becausethey each encode the identical information.

Labels presented on different substrates yet encode the same informationmay be considered the identical “static” label. For example, the labelshown in FIG. 13 may be presented on a storefront window. The labelshown in FIG. 14 may be displayed on an electronic billboard. The labelshown in FIG. 15 may be presented during a TV commercial or embedded ina video stream.

FIG. 16 shows illustrative scenario 1600. In scenario 1600, users 1601,1602, 1604 scan label 901 (also shown in FIG. 9) affixed to window 906of pizza restaurant 907. Scenario 1600 shows that redirect service 913formulates content that will be presented on mobile device 1603 inresponse to user 1601 scanning label 901. Content 1605 includes a targetlanding page about “John Doe.” John Doe may be a founder or proprietorof pizza restaurant 907. Redirect service 913 may formulate content 1605based on one or more scan details associated with the scan of label 901by mobile device 1603.

It should be noted that FIG. 16 also shows redirect services 1617 and1619 which may formulate content (not shown) based on one or more scandetails associated with the scan of label 901 by mobile devices 1606 and1608. The content 1605 shown on device 1603 may be the same as ordifferent from the content shown on devices 1621 and 1623.

An example of showing content based on scan details follows. User 1601may have scanned label 901 at time when pizza restaurant 907 is closed.Redirect service 913 may determine that user 1601 has dietaryrestrictions and it is unlikely user 1601 is interested in ordering foodfrom pizza restaurant 907. Redirect service 913 may determine that user1601 has published various biographical articles and may be interestedin the founder of pizza restaurant 907. Redirect service 913 maydetermine user 1601 has recently searched online for informationregarding the founder of pizza restaurant 907. Content 1605 formulatedby redirect service 913 for user 1601 includes links to informationassociated with John Doe, the founder or proprietor of pizza restaurant907. Such links provide access to information about John Does, includingmusic 1607, social media profile(s) 1609, videos 1611, photos 1613 andJohn Doe's personal homepage 1615.

FIG. 17 shows illustrative scenario 1700. Scenario 1700 shows that user1701 has scanned label 901 (also shown in FIGS. 9 and 16) printed ont-shirt 1707 using mobile device 1703. Scenario 1700 shows that redirectservice 913 formulates content that will be presented on mobile device1703 in response to user 1701 scanning label 901. Scenario 1700 showsthat user 1701 (in contrast to user 909 or user 1601) is presented withcontent 1705. Content 1705 includes a target landing page about “JohnDoe.” John Doe may be a founder or proprietor of pizza restaurant 907.Redirect service 913 may formulate content 1705 based on one or morescan details associated with the scan of label 901 by mobile device1703.

Both content 1605 and content 1705 include links to informationassociated with John Doe. Both content 1605 and content 1705 includelinks to music 1607, social media profile(s) 1609, videos 1611, photos1613 and John Doe's personal homepage 1615. However, the links includedin content 1705 are ordered differently than the links in content 1605.

Links displayed to user 1601 may be ordered based on an intent indexscore of each link computed by redirect service 913 for user 1601. Theorder of links in content 1605 may be determined by scan event detailscaptured by mobile device 1603 a CIG or a user profile associated withuser 1601. The order of links presented in content 1605 may bedetermined based on a response to an earlier scan by mobile device 1603that triggered a display of a target landing page that includedinformation about John Doe. The links associated with content 1605 maybe ordered based on actions of other users (e.g., user 1701) whenviewing a target landing page about John Doe (e.g., content 1705).

Links displayed to user 1701 may be ordered based on an intent indexscore of each link computed by redirect service 913 for user 1701. Theorder of links in content 1705 may be determined by scan event detailscaptured by mobile device 1703 a CIG or a user profile associated withuser 1701. The order of links in content 1705 may be determined based ona response to an earlier scan by mobile device 1703 that triggered adisplay of a target landing page that included information about JohnDoe. The links associated with the target webpage may be ordered basedon actions of other users (e.g., user 1601) when viewing a targetlanding page about John Doe (e.g., content 1605).

FIG. 18 shows illustrative performance metrics 1801 and 1803 that may becaptured based on user activity associated with the target landing pageabout John Doe (e.g., content 1605 and content 1705). Performancemetrics 1800 may be captured based on tracking code embedded in a targetlanding page about John Doe (e.g., content 1605 or content 1705).Performance metrics 1801 show statistics associated with activity ofvisitors on the target landing page about John Doe. Performance metrics1803 show user activity with respect to links 1607, 1609, 1611, 1613 and1615 included within content 1605 and content 1705. Performance metrics1805 may relate to a number of users that have scanned label 901 andpresentation to the scanning users of content about John Doe.

A target landing page associated with a scan of a label may be changedbased on performance metrics (e.g., performance metrics 1800) associatedwith a landing page presented in response to the scan. Illustrativeperformance metrics associated with a landing page may include timeusers spend on a presented landing page and user engagement with contentpresented on a landing (interaction with a chatbot or add to cart orother activity available on landing page). In response to a scan of alabel, methods may include directing users to a target landing page thatis associated with target performance metrics.

It is intended that this disclosure, together with the drawings, beconsidered exemplary only, where exemplary means an example. While thisdisclosure contains many specifics, these should not be construed aslimitations, but rather as descriptions of features that may be specificto particular embodiments. Certain features that are described indisclosure in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a sub combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in this patent document should not be understoodas requiring such separation in all embodiments.

Thus, methods and apparatus for REFACTORING OF STATIC MACHINE-READABLECODES are provided. Persons skilled in the art will appreciate that thepresent disclosure can be practiced by other than the describedembodiments, which are presented for purposes of illustration ratherthan of limitation, and that the present disclosure is limited only bythe claims that follow.

1. An artificial intelligence (“AI”) method for providing a cohort ofusers with a customized digital response to scanning a staticmachine-readable optical label, the AI method comprising: receiving,from a first member of the cohort of users, a first scan of the staticmachine-readable optical label captured using a camera of a mobiledevice, the first scan associated with a first window of time and afirst location of the first member; determining, in real-time based on auser cohort profile, the first window of time, and the first location, afirst landing page URL; redirecting a browser on the mobile device tothe first landing page URL; receiving, from a second member associatedwith a second mobile device, a second scan of the staticmachine-readable optical label, the second scan associated with a secondwindow of time and a second location of the second member; determining,in real-time and based on the user cohort profile, the second time, andthe second location, a second landing page URL; and redirecting thebrowser on the second mobile device to a second landing page URL, thesecond landing page URL that is different from the first landing pageURL.
 2. The AI method of claim 1 further comprising utilizing a redirectservice at a remote location from the first and second mobile devices todetermine the first and the second landing page URLs, respectively. 3.The AI method of claim 1 further comprising: after the first scan,incorporating the first window of time, the first location and the firstlanding page URL into the user cohort profile; and after the secondscan, incorporating the second window of time, the second location andthe second landing page URL into the user cohort profile.
 4. The AImethod of claim 3, wherein the static machine-readable optical label isa first static machine-readable optical label, the method furthercomprising: receiving, from the member, a scan of a second staticmachine-readable optical label; based on the second user cohort profile,determining in real-time a third landing page URL; and redirecting thebrowser on the mobile device to third landing page URL.
 5. The AI methodof claim 3, wherein real time is ≤100 milliseconds from a time of thefirst scan of the static machine-readable optical label captured usingthe camera of the mobile device.
 6. The AI method of claim 4 furthercomprising: receiving, from the first member, a third scan of the firststatic machine-readable optical label; and redirecting the browser onthe first mobile device to the third target landing page.
 7. Anartificial intelligence (“AI”) method for dynamically redirecting aplurality of scans of a static machine-readable optical label todifferent landing pages, the method comprising: receiving a first scanof the static machine-readable optical label from a first mobile deviceof a first group of mobile devices, said first group of mobile deviceshaving a first location; in response to the first scan, generating afirst target landing page URL in real-time in response to the firstscan; redirecting a first browser of the first mobile device to thefirst target landing page URL; receiving, from a second mobile device ofa second group of mobile devices, said second group of mobile deviceshaving a second location, a second scan of the static machine-readableoptical label; in response to the second scan, generating a secondtarget landing page URL in real-time from the second scan; andredirecting a second browser of the second mobile device to the secondtarget landing page URL; wherein, the first scan is associated with afirst time and the second scan is associated with a second time. 8.(canceled)
 9. The AI method of claim 7 further comprising: receiving athird scan of the static machine-readable optical label at a second timefrom the first group of mobile devices; in response to the third scan,generating in real-time based on the third scan, the second landing pageURL; and redirecting the browsers of the first group of mobile devicesto the second landing page corresponding to the second landing page URL.10. (canceled)
 11. The AI method of claim 7 wherein the first scanreceived from the first group of mobile devices and the second scanreceived from the second group of mobile devices capture identicalinformation from the static machine-readable label.
 12. The AI method ofclaim 7, wherein the first landing page URL is different from the secondlanding page URL.
 13. The AI method of claim 7, wherein the firstlanding page URL is identical to the second landing page URL upon adetermination that a first user group profile associated with the firstgroup of mobile devices shares at least one characteristic with a seconduser group profile associated with the second group of mobile devices.14. The AI method of claim 7, further comprising in response toreceiving the first scan from the first mobile device, applying aredirect service that dynamically updates a user profile associated withthe first mobile device based on input received from the first browserwhen navigating the first landing page URL.
 15. The AI method of claim14 further comprising updating a user cohort profile based on thelocation of the first group of mobile devices, and browsing historystored in the first browser.
 16. An artificial intelligence (“AI”)redirect system for dynamically generating a customized landing page inresponse to scanning a static machine-readable optical label, theredirect system comprising a processor and a non-transitory memory withinstructions stored thereon, wherein the instructions upon execution bythe processor, cause the processor to: extract a default UniformResource Locator (URL) from a first scan of the static machine-readableoptical label; receive a first set of scan event details associated withthe first scan of the machine-readable optical label, wherein one of theevent details associated with the first scan is a first user devicelocation; generate a first redirect URL for the first scan based on theset of scan event details; trigger loading of first content linked tothe first redirect URL in response to the first scan; extract thedefault Uniform Resource Locator (URL) from a second scan of the staticmachine-readable optical label; receive a second set of scan eventdetails associated with the second scan of the static machine-readableoptical label, wherein one of the event details associated with thesecond scan is a second user device location; generate a second redirectURL for the second scan based on the second set of scan event details;and trigger loading of a second content linked to the second directredirect URL in response to the second scan.
 17. The AI redirect systemof claim 16 wherein: the first scan is received from one of a firstgroup of mobile devices; the loading of the first content is triggeredon the one of the first mobile devices; the second scan is received fromone of a group of second mobile devices; and the loading of the secondcontent is triggered on the one of the second group of mobile devices.18. The AI redirect system of claim 16 wherein the content linked to thefirst redirect URL is different from the content linked to the redirectURL.
 19. The AI redirect system of claim 16 wherein the instructionsupon execution by the processor, further cause the processor to:transmit tracking code in response to receiving the first scan; receivea third scan of a second static machine-readable optical label; generatea multi-dimensional matrix for a first group of mobile devices based on:the first and second sets of scan event details; and a third set of scanevent details captured by the tracking code; generate a customizedlanding page for the first group of mobile devices in response to thethird scan; generate a third redirect URL for the customized landingpage; and trigger loading of the customized landing page by transmittingthe third redirect URL to the first group of mobile devices in responseto the third scan.
 20. The AI redirect system of claim 16 wherein: thefirst landing page is customized based on one or more first scan eventdetails captured in connection with the first scan; and the secondlanding page is customized based on one or more second scan eventdetails captured in connection with the second scan.
 21. The AI redirectsystem of claim 16 wherein, the second content linked to the secondredirect URL comprises a reordering of links presented in the firstcontent linked to the first redirect URL.
 22. An artificial intelligence(“AI”) method for providing a cohort of users with a customized digitalresponse to scanning a static machine-readable optical label, the AImethod comprising: receiving a first scan of the static machine-readableoptical label captured using a camera of a first mobile device, thefirst scan associated with a first mobile device location; determining,in real-time based on the first location, a first landing page URL;redirecting a browser on the first mobile device to the first landingpage URL; receiving a second scan of the static machine-readable opticallabel captured using a camera of a second mobile device, the second scanassociated with a second location; determining, in real-time and basedon the second location, a second landing page URL; and redirecting abrowser on the second mobile device to the second landing page URL, saidsecond landing page URL that is different from the first target landingpage URL.